class_docstr: This class handles restoring the model at the beginning of a session.
class_docstr_tok: ["This","class","handles","restoring","the","model","at","the","beginning","of","a","session","."]
docstr: Randomly initialising all trainable variables defined in
  the default session.
  
  :param _sender:
  :param _unused:
  :return:
docstr_tok: ["Randomly","initialising","all","trainable","variables","defined","in","the","default","session",".",":","param","_sender",":",":","param","_unused",":",":","return",":"]
code: def rand_init_model(self, _sender, **_unused):
      """
      Randomly initialising all trainable variables defined in
      the default session.
  
      :param _sender:
      :param _unused:
      :return:
      """
      with tf.name_scope("Initialisation"):
          init_op = global_vars_init_or_restore()
      tf.get_default_session().run(init_op)
      tf.logging.info("Parameters from random initialisations ...")
  
file_path: NiftyNet/niftynet/engine/handler_model.py
imports: import os
  import tensorflow
  from niftynet.engine.application_variables import global_vars_init_or_restore
  from niftynet.engine.signal import ITER_FINISHED, SESS_FINISHED, SESS_STARTED
  from niftynet.io.misc_io import touch_folder
label_desc: Use image node_id 2 for calling the ModelRestorer obj's underlying member method code with example usage: obj.rand_init_model(_sender) without return types